chatbot_using_GPT_index,llamaindex_and_Langchain.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/siddharthc30/Plant-Disease-Chatbot/blob/main/chatbot_using_GPT_index%2Cllamaindex_and_Langchain.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v_iKCHr6fx9L",
        "outputId": "ef07eefd-0cdd-4cbe-fd9a-440d1bb64842"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "Building wheels for collected packages: llama_index\n",
            "  Building wheel for llama_index (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for llama_index: filename=llama_index-0.5.3-py3-none-any.whl size=245716 sha256=4dae0100d05e422beb4ebe8ff16a78848ae1ec1de1bbdb5ac254242f1490e077\n",
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            "Installing collected packages: llama_index\n",
            "Successfully installed llama_index-0.5.3\n"
          ]
        }
      ],
      "source": [
        "!pip install gpt_index\n",
        "!pip install langchain==0.0.127\n",
        "!pip install llama_index==0.5.3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iEcc9AGiJEyz"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EDycel97gAdv"
      },
      "outputs": [],
      "source": [
        "#from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper\n",
        "from langchain import OpenAI\n",
        "from llama_index import SimpleDirectoryReader, LangchainEmbedding, GPTListIndex,GPTSimpleVectorIndex, PromptHelper\n",
        "from llama_index import LLMPredictor, ServiceContext\n",
        "import sys\n",
        "#from google.colab import drive\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZicDwO1Mgb8I"
      },
      "outputs": [],
      "source": [
        "os.environ[\"OPENAI_API_KEY\"] = ''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5OWgRYxDhHoq"
      },
      "outputs": [],
      "source": [
        "def construct_index(directory_path):\n",
        "  # set maximum input size\n",
        "  max_input_size = 4096\n",
        "  # set number of output tokens\n",
        "  num_outputs = 256\n",
        "  # set maximum chunk overlap\n",
        "  max_chunk_overlap = 20\n",
        "  # set chunk size limit\n",
        "  chunk_size_limit = 600\n",
        "\n",
        "  prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)\n",
        "\n",
        "  # define LLM\n",
        "  llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, model_name=\"text-davinci-002\", max_tokens=num_outputs))\n",
        "\n",
        "  documents = SimpleDirectoryReader(directory_path).load_data()\n",
        "\n",
        "  service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor)\n",
        "  index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)\n",
        "\n",
        "  index.save_to_disk('index.json')\n",
        "\n",
        "  return index\n",
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jpTq4c8Njpzy"
      },
      "outputs": [],
      "source": [
        "def ask_bot(input_index = 'index.json'):\n",
        "  index = GPTSimpleVectorIndex.load_from_disk(input_index)\n",
        "  #while True:\n",
        "  query = input('What do you want to ask the bot?   \\n')\n",
        "  response = index.query(query, response_mode=\"compact\")\n",
        "  print (response.response)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rK0l9B4zj_69"
      },
      "outputs": [],
      "source": [
        "index = construct_index(\"/content/\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jXwPSwrpkN5x",
        "outputId": "2db621f5-7027-48dd-e554-4888dc293567"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "What do you want to ask the bot?   \n",
            "what is the name of the disease\n",
            "\n",
            "The name of the disease is Early Leafspot of Groundnut.\n"
          ]
        }
      ],
      "source": [
        "ask_bot('index.json')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w58WCFXW2CGR",
        "outputId": "c0cfa080-18ea-4dba-ac39-adeb13244b39"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->gradio) (1.0.7)\n",
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            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->gradio) (0.11.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->gradio) (1.26.15)\n",
            "Requirement already satisfied: click>=7.0 in /usr/local/lib/python3.10/dist-packages (from uvicorn->gradio) (8.1.3)\n",
            "Collecting h11>=0.8\n",
            "  Downloading h11-0.14.0-py3-none-any.whl (58 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: anyio<5.0,>=3.0 in /usr/local/lib/python3.10/dist-packages (from httpcore<0.18.0,>=0.15.0->httpx->gradio) (3.6.2)\n",
            "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair>=4.2.0->gradio) (0.19.3)\n",
            "Collecting uc-micro-py\n",
            "  Downloading uc_micro_py-1.0.2-py3-none-any.whl (6.2 kB)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->gradio) (1.16.0)\n",
            "Building wheels for collected packages: ffmpy\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4707 sha256=bf8195b4bacd508ddbc4b5e6c60a13e01d9dbb8ae37991ae4fce0f1ecd56f777\n",
            "  Stored in directory: /root/.cache/pip/wheels/0c/c2/0e/3b9c6845c6a4e35beb90910cc70d9ac9ab5d47402bd62af0df\n",
            "Successfully built ffmpy\n",
            "Installing collected packages: pydub, ffmpy, websockets, uc-micro-py, semantic-version, python-multipart, orjson, h11, aiofiles, uvicorn, starlette, mdit-py-plugins, linkify-it-py, huggingface-hub, httpcore, httpx, fastapi, gradio-client, gradio\n",
            "Successfully installed aiofiles-23.1.0 fastapi-0.95.1 ffmpy-0.3.0 gradio-3.28.3 gradio-client-0.2.1 h11-0.14.0 httpcore-0.17.0 httpx-0.24.0 huggingface-hub-0.14.1 linkify-it-py-2.0.2 mdit-py-plugins-0.3.3 orjson-3.8.12 pydub-0.25.1 python-multipart-0.0.6 semantic-version-2.10.0 starlette-0.26.1 uc-micro-py-1.0.2 uvicorn-0.22.0 websockets-11.0.3\n"
          ]
        }
      ],
      "source": [
        "!pip install gradio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "87YHcoRdIfbe"
      },
      "outputs": [],
      "source": [
        "import gradio as gr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YycE1MTt9g--"
      },
      "outputs": [],
      "source": [
        "def chat(chat_history, user_input, input_index = 'index.json'):\n",
        "\n",
        "  index = GPTSimpleVectorIndex.load_from_disk(input_index)\n",
        "  bot_response = index.query(user_input, response_mode=\"compact\")\n",
        "  #print(bot_response)\n",
        "  response = \"\"\n",
        "  for letter in ''.join(bot_response.response): #[bot_response[i:i+1] for i in range(0, len(bot_response), 1)]:\n",
        "      response += letter + \"\"\n",
        "      yield chat_history + [(user_input, response)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "id": "AZD6DoVd2Vaf",
        "outputId": "61328b18-b96f-4913-eb91-fdf05da860c1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
            "Running on public URL: https://adb1cfbd36d1b049df.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div><iframe src=\"https://adb1cfbd36d1b049df.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "with gr.Blocks() as demo:\n",
        "  with gr.Tab(\"Knowledge Bot\"):\n",
        "#          inputbox = gr.Textbox(\"Input your text to build a Q&A Bot here.....\")\n",
        "          chatbot = gr.Chatbot()\n",
        "          message = gr.Textbox (\"What is this document about?\")\n",
        "          message.submit(chat, [chatbot, message], chatbot)\n",
        "\n",
        "demo.queue().launch(debug = True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-2XoUY_85oS5"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ltyv6mjGnWKy"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lOgcgskO18Vg"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOhgheGSvZNM0LCj/oKj/H6",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}